Age prediction is an important part of medical assessments and research. It can aid in detecting diseases as well as abnormal ageing by highlighting the discrepancy between chronological and biological age. To gain a comprehensive understanding of age-related changes observed in various body parts, we investigate them on a larger scale by using whole-body images. We utilise the Grad-CAM interpretability method to determine the body areas most predictive of a person's age. We expand our analysis beyond individual subjects by employing registration techniques to generate population-wide interpretability maps. Furthermore, we set state-of-the-art whole-body age prediction with a model that achieves a mean absolute error of 2.76 years. Our findings reveal three primary areas of interest: the spine, the autochthonous back muscles, and the cardiac region, which exhibits the highest importance.

翻译：年龄预测是医学评估与研究的重要组成部分。通过揭示时序年龄与生物学年龄之间的差异，年龄预测有助于疾病检测及异常衰老识别。为全面理解人体各部位随年龄变化所呈现的规律，我们采用全身图像进行大尺度研究。运用Grad-CAM可解释性方法，我们确定了最能预测个体年龄的身体区域。借助配准技术生成群体层面的可解释性热力图，我们将分析范围从个体扩展至群体。进一步地，我们建立了全身年龄预测的最新基准模型，其平均绝对误差达2.76年。研究结果揭示了三大关键区域：脊柱、背部固有肌群以及重要性最高的心脏区域。